title: Using electronic health record data to develop inpatient mortality predictive model: Acute Laboratory Risk of Mortality Score (ALaRMS).
abstract: OBJECTIVE
  Using numeric laboratory data and administrative data from hospital electronic health record (EHR) systems, to develop an inpatient mortality predictive model.
  
  
  METHODS
  Using EHR data of 1,428,824 adult discharges from 70 hospitals in 2006-2007, we developed the Acute Laboratory Risk of Mortality Score (ALaRMS) using age, gender, and initial laboratory values on admission as candidate variables. We then added administrative variables using the Agency for Healthcare Research and Quality (AHRQ)'s clinical classification software (CCS) and comorbidity software (CS) as disease classification tools. We validated the model using 770,523 discharges in 2008.
  
  
  RESULTS
  Mortality predictors with ORs >2.00 included age, deranged albumin, arterial pH, bands, blood urea nitrogen, oxygen partial pressure, platelets, pro-brain natriuretic peptide, troponin I, and white blood cell counts. The ALaRMS model c-statistic was 0.87. Adding the CCS and CS variables increased the c-statistic to 0.91. The relative contributions were 69% (ALaRMS), 25% (CCS), and 6% (CS). Furthermore, the integrated discrimination improvement statistic demonstrated a 127% (95% CI 122% to 133%) overall improvement when ALaRMS was added to CCS and CS variables. In contrast, only a 22% (CI 19% to 25%) improvement was seen when CCS and CS variables were added to ALaRMS.
  
  
  CONCLUSIONS
  EHR data can generate clinically plausible mortality predictive models with excellent discrimination. ALaRMS uses automated laboratory data widely available on admission, providing opportunities to aid real-time decision support. Models that incorporate laboratory and AHRQ's CCS and CS variables have utility for risk adjustment in retrospective outcome studies.
authors.affiliation: Department of Clinical Research, CareFusion, San Diego, California, USA.
authors.forename: Ying P
authors.initials: YP
authors.lastname: Tabak
authors.forename: Xiaowu
authors.initials: X
authors.lastname: Sun
authors.forename: Carlos M
authors.initials: CM
authors.lastname: Nunez
authors.forename: Richard S
authors.initials: RS
authors.lastname: Johannes
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D057286:Electronic Health Records; D005260:Female; D006403:Hematologic Tests; D017052:Hospital Mortality; D006760:Hospitalization; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011379:Prognosis; D018570:Risk Assessment / Q000379:methods*; D012984:Software
keywords: Decision Support; Electronic Health Record (EHR); Laboratory Data; Mortality Risk Model; Outcome Research